Digger Finger: GelSight Tactile Sensor for Object Identification Inside Granular Media
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Abstract (arXiv)
In this paper we present an early prototype of the Digger Finger that is designed to easily penetrate granular media and is equipped with the GelSight sensor. Identifying objects buried in granular media using tactile sensors is a challenging task. First, particle jamming in granular media prevents downward movement. Second, the granular media particles tend to get stuck between the sensing surface and the object of interest, distorting the actual shape of the object. To tackle these challenges we present a Digger Finger prototype. It is capable of fluidizing granular media during penetration using mechanical vibrations. It is equipped with high resolution vision based tactile sensing to identify objects buried inside granular media. We describe the experimental procedures we use to evaluate these fluidizing and buried shape recognition capabilities. A robot with such fingers can perform explosive ordnance disposal and Improvised Explosive Device (IED) detection tasks at a much a finer resolution compared to techniques like Ground Penetration Radars (GPRs). Sensors like the Digger Finger will allow robotic manipulation research to move beyond only manipulating rigid objects.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2021.
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Citing papers per year: 2022: 1; 2023: 3; 2024: 1; 2025: 1; 2026: 2.
8 papers.
- 2023Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Playcited by 60
- 2023See to Touch: Learning Tactile Dexterity through Visual Incentivescited by 33
- 2022DenseTact: Optical Tactile Sensor for Dense Shape Reconstructioncited by 13
- 2024Tactile-based Object Retrieval From Granular Mediacited by 4
- 2023Towards Design and Development of an ArUco Markers-Based Quantitative Surface Tactile Sensorcited by 0
- 2025A Modularized Design Approach for GelSight Family of Vision-based Tactile Sensorscited by 0
- 2026TransDex: Pre-training Visuo-Tactile Policy with Point Cloud Reconstruction for Dexterous Manipulation of Transparent Objectscited by 0
- 2026DeCAL: Towards Physically-Grounded Dexterous Vision-Language-Action Models via Contact-Aware Latent Co-Imaginationcited by 0
- 2026DeCAL: Towards Physically-Grounded Dexterous Vision-Language-Action Models via Contact-Aware Latent Co-Imagination8 Sep 2026
- 2026TransDex: Pre-training Visuo-Tactile Policy with Point Cloud Reconstruction for Dexterous Manipulation of Transparent Objects14 Mar 2026
- 2025A Modularized Design Approach for GelSight Family of Vision-based Tactile Sensors20 Apr 2025
- 2024Tactile-based Object Retrieval From Granular Media7 Feb 2024
- 2023Towards Design and Development of an ArUco Markers-Based Quantitative Surface Tactile Sensor12 Oct 2023
- 2023See to Touch: Learning Tactile Dexterity through Visual Incentives21 Sep 2023
- 2023Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play21 Mar 2023
- 2022DenseTact: Optical Tactile Sensor for Dense Shape Reconstruction4 Jan 2022